Artificial Intelligence Data Engineer

About Everest:

Everest Group, Ltd. (Everest), is a leading global reinsurance and insurance provider, operating for nearly 50 years through subsidiaries in North America, Latin America, the UK & Ireland, Continental Europe and Asia Pacific regions. Throughout our history, Everest has maintained its discipline and focuses on creating long-term value through underwriting excellence and strong risk and capital management. Our strengths include extensive product and distribution capabilities, a strong balance sheet, and an innovative culture. Our most critical asset is our people. We offer dynamic training & professional development to our employees. We also offer generous tuition/continuing education reimbursement programs, mentoring opportunities, flexible work arrangements, and Colleague Resource Groups.

• Develop and Maintain Data Infrastructure: Design, build, and maintain scalable data infrastructure to support AI models and other data-driven applications.

• Data Ingestion and Transformation: Implement efficient data ingestion and transformation processes to ensure high-quality, accessible data for AI models.

• Data Storage Management: Optimize data storage solutions to handle large volumes of data efficiently and securely.

• Scalability and Reliability: Ensure data pipelines are scalable and reliable, capable of handling increasing data loads and complex AI requirements.

• Collaboration with AI Team: Work closely with data scientists, AI engineers, and other stakeholders to understand data requirements and deliver solutions that meet their needs.

• Performance Optimization: Continuously monitor and optimize data pipelines for performance, cost-efficiency, and alignment with AI model needs.

• Documentation and Best Practices: Maintain comprehensive documentation of data processes and adhere to best practices in data engineering and AI integration.

• SQL: Proficiency in SQL for querying and managing relational databases.

• Python: Strong programming skills in Python for data manipulation, automation, and integration with AI frameworks.

• ETL Pipelines: Experience in designing and implementing ETL (Extract, Transform, Load) pipelines tailored for AI data workflows. Building pipelines is a plus

• Cloud Platforms: Familiarity with cloud platforms such as Azure, or Google Cloud for data storage, processing, and AI model deployment. Databricks experience is a plus

• Big Data Technologies: Knowledge of big data technologies like Apache Spark and Hadoop for handling large datasets and supporting AI model training.

• AI Data Engineering: Understanding of data engineering principles specific to AI, including data preprocessing, feature engineering, and integration with machine learning frameworks.

• Insurance Data Knowledge: Understanding of insurance data and industry-specific requirements is a plus.

• Education: A degree in Computer Science, Engineering, Data Science, or a related field is preferred.

• Experience: Previous experience in a similar role, especially within the AI or insurance industry, is highly desirable.

• Certifications: Relevant certifications in data engineering, cloud platforms, or AI technologies can be advantageous.

• Soft Skills: Strong problem-solving skills, attention to detail, and the ability to work collaboratively in a team environment.